Transmission Investment Coordination using MILP Lagrange Dual Decomposition and Auxiliary Problem Principle

This paper considers the investment coordination problem for the long term transmission capacity expansion in a situation where there are multiple regional Transmission Planners (TPs), each acting in order to maximize the utility in only its own region. In such a setting, any particular TP does not normally have any incentive to cooperate with the neighboring TP(s), although the optimal investment decision of each TP is contingent upon those of the neighboring TPs. A game-theoretic interaction among the TPs does not necessarily lead to this overall social optimum. We, therefore, introduce a social planner and call it the Transmission Planning Coordinator (TPC) whose goal is to attain the optimal possible social welfare for the bigger geographical region. In order to achieve this goal, this paper introduces a new incentive mechanism, based on distributed optimization theory. This incentive mechanism can be viewed as a set of rules of the transmission expansion investment coordination game, set by the social planner TPC, such that, even if the individual TPs act selfishly, it will still lead to the TPC's goal of attaining overall social optimum. Finally, the effectiveness of our approach is demonstrated through several simulation studies

Post-contingency states representation and redispatch for restoration in power systems operation

In this treatise, we will present a dynamic version of the Security Constrained Optimal Power Flow (SCOPF) problem, the "Look Ahead Security Constrained Optimal Power Flow" (LASCOPF) problem with post-contingency states representation and redispatch scheme for restoration to normal operation, following an outage represented in the mathematical formulation. We will also propose a distributed algorithm to solve the OPF, SCOPF, and LASCOPF problems. The objective of the problem is to minimize the cost of operation, over a number of dispatch intervals and across all contingency scenarios subject to the constraints of the network. It is, therefore, a large optimization problem, requiring an effective distributed solution method. As one of the means to address this challenge, we will be extending the Proximal Message Passing (PMP) algorithmic framework, which is based on another algorithm, called Alternating Direction Method of Multipliers (ADMM) and combine it with the Auxiliary Problem Principle (APP). The resulting algorithm, which we hereafter will call Auxiliary Proximal Message Passing (APMP) is extremely scalable with respect to both network size and the number of scenarios. We implement a look-ahead contingency planning, representing the post-contingency states of the system ahead of time, in a Receding Horizon Control (RHC) or, Model Predictive Control (MPC) type of formulation. One goal of this work is to particularly focus our attention on the trajectories of post-contingency line temperature rise, line MW flow rise, and line current rise and try to limit them through our proposed method. We also investigate how to reduce the computational burden. The reason for paying particular attention to line temperature rise and limiting the same, is the intention of the present scheme to make the most use of the existing transmission capability, without costly transmission upgrades. The means of attaining that goal is to make use of short term thermal overload rating and dynamic thermal limit, and in the event of an actual outage, modifying the dispatch in such a way, that the flows on the remaining lines can be brought back to within allowed values in a given time interval. We demonstrate the effectiveness of our distributed method with a series of numerical simulations based on some simple systems and the IEEE test systems. Finally, we conclude, with a suggestion to some possible future research directions.Electrical and Computer Engineerin

Study of UPLAN based resources planning & analysis by power generation utilities in the deregulated electricity market

textGenerators bid into the deregulated electricity market in order to get committed & dispatched for meeting demands. In order to maximize their revenues & minimize the cost, systematic planning of the resources and analyzing the results is crucial to the success of any generation utility. UPLAN Network Power Model provides a convenient way to model & simulate the different expected conditions related to transmission, fuel costs & other variables which are of significant importance for generation planning and also allows us to analyze the way the output variables like capacity factors of generators, prices for Energy and Ancillary Services are affected by them. Based on a very simple model, this report describes the typical approach to UPLAN based resources planning & analyzes the significance of the results. Before that it also tried to understand the way UPLAN works for a very simple three bus model by stepwise introduction of complexity & analysis of results of the simulation runs. A few other issues like the Power Purchase Agreements, Congestion & Congestion Revenue Rights & the way Electricity is traded in the Deregulated Market are also presented.Electrical and Computer Engineerin

Toward Distributed/Decentralized DC Optimal Power Flow Implementation in Future Electric Power Systems

This paper reviews distributed/decentralized algorithms to solve the optimal power flow (OPF) problem in electric power systems. Six decomposition coordination algorithms are studied, including analytical target cascading, optimality condition decomposition, alternating direction method of multipliers, auxiliary problem principle, consensus+innovations, and proximal message passing. The basic concept, the general formulation, the application for dc-OPF, and the solution methodology for each algorithm are presented. We apply these six decomposition coordination algorithms on a test system, and discuss their key features and simulation results

A Survey of Distributed Optimization and Control Algorithms for Electric Power Systems

Historically, centrally computed algorithms have been the primary means of power system optimization and control. With increasing penetrations of distributed energy resources requiring optimization and control of power systems with many controllable devices, distributed algorithms have been the subject of significant research interest. This paper surveys the literature of distributed algorithms with applications to optimization and control of power systems. In particular, this paper reviews distributed algorithms for offline solution of optimal power flow (OPF) problems as well as online algorithms for real-time solution of OPF, optimal frequency control, optimal voltage control, and optimal wide-area control problems